OpenRaiser/Intern-Atlas huggingface dataset:
A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweets
Venue: arXiv cs.CV 2022
Year: 2022

People have recently begun communicating their thoughts and viewpoints through user-generated multimedia material on social networking websites. This information can be images, text, videos, or audio. Recent years have seen a rise in the frequency of occurrence of this pattern. Twitter is one of the most extensively utilized social media sites, and it is also one of the finest locations to get a sense of how people feel about events that are linked to the Monkeypox sickness. This is because tweets on Twitter are shortened and often updated, both of which contribute to the platform's character. The fundamental objective of this study is to get a deeper comprehension of the diverse range of reactions people have in response to the presence of this condition. This study focuses on finding out what individuals think about monkeypox illnesses, which presents a hybrid technique based on CNN and LSTM. We have considered all three possible polarities of a user's tweet: positive, negative, and neutral. An architecture built on CNN and LSTM is utilized to determine how accurate the prediction models are. The recommended model's accuracy was 94% on the monkeypox tweet dataset. Other performance metrics such as accuracy, recall, and F1-score were utilized to test our models and results in the most time and resource-effective manner. The findings are then compared to more traditional approaches to machine learning. The findings of this research contribute to an increased awareness of the monkeypox infection in the general population.
Performance Analysis for Reconfigurable Intelligent Surface Assisted MIMO Systems
Venue: arXiv cs.IT 2022
Year: 2022

This paper investigates the maximal achievable rate for a given average error probability and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength channel coding achievability bound and a converse bound based on the Berry-Esseen theorem, the Mellin transform and the mutual information. Numerical evaluation shows fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength.
Two-stage Fall Events Classification with Human Skeleton Data
Venue: arXiv cs.CV 2022
Year: 2022

Fall detection and classification become an imper- ative problem for healthcare applications particularity with the increasingly ageing population. Currently, most of the fall clas- sification algorithms provide binary fall or no-fall classification. For better healthcare, it is thus not enough to do binary fall classification but to extend it to multiple fall events classification. In this work, we utilize the privacy mitigating human skeleton data for multiple fall events classification. The skeleton features are extracted from the original RGB images to not only mitigate the personal privacy, but also to reduce the impact of the dynamic illuminations. The proposed fall events classification method is divided into two stages. In the first stage, the model is trained to achieve the binary classification to filter out the no-fall events. Then, in the second stage, the deep neural network (DNN) model is trained to further classify the five types of fall events. In order to confirm the efficiency of the proposed method, the experiments on the UP-Fall dataset outperform the state-of-the-art.
Evaluating Conversational Recommender Systems: A Landscape of Research
Venue: arXiv cs.IR 2022
Year: 2022

Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices.
Learning to regulate 3D head shape by removing occluding hair from in-the-wild images
Venue: arXiv cs.CV 2022
Year: 2022

Recent 3D face reconstruction methods reconstruct the entire head compared to earlier approaches which only model the face. Although these methods accurately reconstruct facial features, they do not explicitly regulate the upper part of the head. Extracting information about this part of the head is challenging due to varying degrees of occlusion by hair. We present a novel approach for modeling the upper head by removing occluding hair and reconstructing the skin, revealing information about the head shape. We introduce three objectives: 1) a dice consistency loss that enforces similarity between the overall head shape of the source and rendered image, 2) a scale consistency loss to ensure that head shape is accurately reproduced even if the upper part of the head is not visible, and 3) a 71 landmark detector trained using a moving average loss function to detect additional landmarks on the head. These objectives are used to train an encoder in an unsupervised manner to regress FLAME parameters from in-the-wild input images. Our unsupervised 3DMM model achieves state-of-the-art results on popular benchmarks and can be used to infer the head shape, facial features, and textures for direct use in animation or avatar creation.
Training a T5 Using Lab-sized Resources
Venue: arXiv cs.CL 2022
Year: 2022

Training large neural language models on large datasets is resource- and time-intensive. These requirements create a barrier to entry, where those with fewer resources cannot build competitive models. This paper presents various techniques for making it possible to (a) train a large language model using resources that a modest research lab might have, and (b) train it in a reasonable amount of time. We provide concrete recommendations for practitioners, which we illustrate with a case study: a T5 model for Danish, the first for this language.
UAS Navigation in the Real World Using Visual Observation
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a novel end-to-end Unmanned Aerial System (UAS) navigation approach for long-range visual navigation in the real world. Inspired by dual-process visual navigation system of human's instinct: environment understanding and landmark recognition, we formulate the UAS navigation task into two same phases. Our system combines the reinforcement learning (RL) and image matching approaches. First, the agent learns the navigation policy using RL in the specified environment. To achieve this, we design an interactive UASNAV environment for the training process. Once the agent learns the navigation policy, which means 'familiarized themselves with the environment', we let the UAS fly in the real world to recognize the landmarks using image matching method and take action according to the learned policy. During the navigation process, the UAS is embedded with single camera as the only visual sensor. We demonstrate that the UAS can learn navigating to the destination hundreds meters away from the starting point with the shortest path in the real world scenario.
Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study
Venue: arXiv cs.CR 2022
Year: 2022

Proactive approaches to security, such as adversary emulation, leverage information about threat actors and their techniques (Cyber Threat Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e., natural language), such as incident reports and leaked documents. To support proactive security efforts, we present an experimental study on the automatic classification of unstructured CTI into attack techniques using machine learning (ML). We contribute with two new datasets for CTI analysis, and we evaluate several ML models, including both traditional and deep learning-based ones. We present several lessons learned about how ML can perform at this task, which classifiers perform best and under which conditions, which are the main causes of classification errors, and the challenges ahead for CTI analysis.
Emergence of group hierarchy
Venue: arXiv cs.MA 2022
Year: 2022

We consider an opinion dynamics model where, during random pair interactions, each agent modifies her opinions about both agents of the random pair and also about some other agents, chosen randomly. Moreover, each agent belongs to a single group and the opinions within the group are attracted to their average. In simulations starting from neutral opinions, we observe the emergence of a group hierarchy. We derive a moment approximation that provides equations ruling the evolution of the average opinion of agents in a group about the agents of another group. This approximation explains how the group hierarchy emerges.
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders
Venue: arXiv cs.LG 2022
Year: 2022

The joint optimization of the reconstruction and classification error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is proposed, in which a Convolutional Autoencoder for dimensionality reduction and a classifier composed by a Fully Connected Network, are combined to simultaneously produce supervised dimensionality reduction and predictions. It turned out that this methodology can also be greatly beneficial in enforcing explainability of deep learning architectures. Additionally, the resulting Latent Space, optimized for the classification task, can be utilized to improve traditional, interpretable classification algorithms. The experimental results, showed that the proposed methodology achieved competitive results against the state of the art deep learning methods, while being much more efficient in terms of parameter count. Finally, it was empirically justified that the proposed methodology introduces advanced explainability regarding, not only the data structure through the produced latent space, but also about the classification behaviour.
Sistematic mapping protocol - estimation accuracy on software development using agile technologies
Venue: arXiv cs.SE 2022
Year: 2022

Protocol for a Systematic Mapping of the Literature, which aims to identify and classify the estimations techniques used in software development agile methodologies based on the results found, and to compare their estimation accuracies against those obtained in traditional software development methodologies.
JAXFit: Trust Region Method for Nonlinear Least-Squares Curve Fitting on the GPU
Venue: arXiv cs.LG 2022
Year: 2022

We implement a trust region method on the GPU for nonlinear least squares curve fitting problems using a new deep learning Python library called JAX. Our open source package, JAXFit, works for both unconstrained and constrained curve fitting problems and allows the fit functions to be defined in Python alone -- without any specialized knowledge of either the GPU or CUDA programming. Since JAXFit runs on the GPU, it is much faster than CPU based libraries and even other GPU based libraries, despite being very easy to use. Additionally, due to JAX's deep learning foundations, the Jacobian in JAXFit's trust region algorithm is calculated with automatic differentiation, rather than than using derivative approximations or requiring the user to define the fit function's partial derivatives.
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Venue: arXiv cs.NE 2022
Year: 2022

Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimization problems with heterogeneous objectives (HE-MOPs). So far, however, only a few studies have been reported to address HE-MOPs, and most of them focus on bi-objective problems with one fast objective and one slow objective. In this work, we aim to deal with HE-MOPs having more than two black-box and heterogeneous objectives. To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives. To make the best use of two different training data sets, one with solutions evaluated on all objectives and the other with those only evaluated on the fast objectives, two separate Gaussian process models are constructed. In addition, a new acquisition function that mitigates search bias towards the fast objectives is suggested, thereby achieving a balance between convergence and diversity. We demonstrate the effectiveness of the proposed algorithm by testing it on widely used multi-/many-objective benchmark problems whose objectives are assumed to be heterogeneously expensive.
SAP Signavio Academic Models: A Large Process Model Dataset
Venue: arXiv cs.OH 2022
Year: 2022

In this paper, we introduce the SAP Signavio Academic Models (SAP-SAM) dataset, a collection of hundreds of thousands of business models, mainly process models in BPMN notation. The model collection is a subset of the models that were created over the course of roughly a decade on academic.signavio.com, a free-of-charge software-as-a-service platform that researchers, teachers, and students can use to create business (process) models. We provide a preliminary analysis of the model collection, as well as recommendations on how to work with it. In addition, we discuss potential use cases and limitations of the model collection from academic and industry perspectives.
A Gis Aided Approach for Geolocalizing an Unmanned Aerial System Using Deep Learning
Venue: arXiv cs.CV 2022
Year: 2022

The Global Positioning System (GPS) has become a part of our daily life with the primary goal of providing geopositioning service. For an unmanned aerial system (UAS), geolocalization ability is an extremely important necessity which is achieved using Inertial Navigation System (INS) with the GPS at its heart. Without geopositioning service, UAS is unable to fly to its destination or come back home. Unfortunately, GPS signals can be jammed and suffer from a multipath problem in urban canyons. Our goal is to propose an alternative approach to geolocalize a UAS when GPS signal is degraded or denied. Considering UAS has a downward-looking camera on its platform that can acquire real-time images as the platform flies, we apply modern deep learning techniques to achieve geolocalization. In particular, we perform image matching to establish latent feature conjugates between UAS acquired imagery and satellite orthophotos. A typical application of feature matching suffers from high-rise buildings and new constructions in the field that introduce uncertainties into homography estimation, hence results in poor geolocalization performance. Instead, we extract GIS information from OpenStreetMap (OSM) to semantically segment matched features into building and terrain classes. The GIS mask works as a filter in selecting semantically matched features that enhance coplanarity conditions and the UAS geolocalization accuracy. Once the paper is published our code will be publicly available at https://github.com/OSUPCVLab/UbihereDrone2021.
Adaptive Learning for Service Monitoring Data
Venue: arXiv cs.LG 2022
Year: 2022

Service monitoring applications continuously produce data to monitor their availability. Hence, it is critical to classify incoming data in real-time and accurately. For this purpose, our study develops an adaptive classification approach using Learn++ that can handle evolving data distributions. This approach sequentially predicts and updates the monitoring model with new data, gradually forgets past knowledge and identifies sudden concept drift. We employ consecutive data chunks obtained from an industrial application to evaluate the performance of the predictors incrementally.
Mask-Mediator-Wrapper: A revised mediator-wrapper architecture for heterogeneous data source integration
Venue: arXiv cs.SE 2022
Year: 2022

This paper deals with the mediator-wrapper architecture. It is an important architectural pattern that enables a more flexible and modular architecture in opposition to monolithic architectures for data source integration systems. This paper identifies certain realistic and concrete scenarios where the mediator-wrapper architecture underperforms. These issues are addressed with the extension of the architecture via the mask component type. The mask component is detailed so it can be reasoned about without prescribing a concrete programming language or paradigm. The benefits of the new mask-mediator-wrapper architecture are analytically proven in relevant scenarios. One of the applications of the new architecture is envisioned for modern data sources integration systems backing Big data processing.
LinCQA: Faster Consistent Query Answering with Linear Time Guarantees
Venue: arXiv cs.DB 2022
Year: 2022

Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. This paper identifies a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis's algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that can output rewritings in both SQL and non-recursive Datalog rules for every query in this class. We show that LinCQA often outperforms the existing CQA systems on both synthetic and real-world workloads, and in some cases, by orders of magnitude.
Subjective data models in bioinformatics: Do wet-lab and computational biologists comprehend data differently?
Venue: arXiv cs.HC 2022
Year: 2022

Biological science produces large amounts of data in a variety of formats, which necessitates the use of computational tools to process, integrate, analyse, and glean insights from the data. Researchers who use computational biology tools range from those who use computers primarily for communication and data lookup, to those who write complex software programs in order to analyse data or make it easier for others to do so. This research examines how people differ in how they conceptualise the same data, for which we coin the term "subjective data models". We interviewed 22 people with biological experience and varied levels of computational experience to elicit their perceptions of the same subset of biological data entities. The results suggest that many people had fluid subjective data models that would change depending on the circumstance or tool they were using. Surprisingly, results generally did not seem to cluster around a participant's computational experience/education levels, or the lack thereof. We further found that people did not consistently map entities from an abstract data model to the same identifiers in real-world files, and found that certain data identifier formats were easier for participants to infer meaning from than others. Real-world implications of these findings suggests that 1) software engineers should design interfaces for task performance and emulate other related popular user interfaces, rather than targeting a person's professional background; 2) when insufficient context is provided, people may guess what data means, whether or not their guesses are correct, emphasising the importance of providing contextual metadata when preparing data for re-use by other, to remove the need for potentially erroneous guesswork.
Snooping on Snoopers: Logging as a Security Response to Physical Attacks on Mobile Devices
Venue: arXiv cs.HC 2022
Year: 2022

When users leave their mobile devices unattended, or let others use them momentarily, they are susceptible to privacy breaches. Existing technological defenses, such as unlock authentication or account switching, have proven to be unpopular. We conducted interviews to uncover practices users currently engage in to cope with the threat, and found that it is common for users to try to keep their devices under close supervision at all times. One obstacle to this strategy is that displaying such protective behavior can be detrimental to social relationships. To address these concerns, we built a software tool that gathers activity logs in the background. Logs can later be reviewed as a timeline of opened apps and the actions performed within each, with events decorated with pictures captured inconspicuously with the front-facing camera. We evaluated this approach in a user study, and found participants to be generally eager to adopt the technology, although in different ways. Most users foresaw using it as a deterrent, or to check if they were snooped on, if that suspicion were ever to arise. Yet, some voiced the intention of creating "honey traps". The results highlight both the opportunities and the potential dangers of the logging approach.
Expansion and evolution of the R programming language
Venue: arXiv cs.PL 2022
Year: 2022

Change in language use is driven by cultural forces; it is unclear whether that extends to programming languages. They are designed to be used by humans, but interaction with computer hardware rather than a human audience may limit opportunities for evolution of the lexicon of used terms. I tested this in R, an open source, mature and commonly used programming language for statistical computing. In corpus of 360,321 GitHub repositories published between 2014 and 2021, I extracted 168,857,044 function calls to act as n-grams of the R language. Over the eight-year period, R rapidly diversified and underwent substantial lexical change, driven by increasing popularity of the tidyverse collection of community packages. My results provide evidence that users can influence the evolution of programming languages, with patterns that match those observed in natural languages and reflect genetic evolution. R's evolution may have been driven by increased analytic complexity, driving new users to R, creating both selective pressure for an alternate lexicon and accompanying advective change. The speed and magnitude of this change may have flow-on consequences for the readability and continuity of analytic and scientific inquiries codified in R and similar languages.